{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "#from tensorflow.models.rnn import rnn, rnn_cell\n",
    "import numpy as np\n",
    "import numpy.random as rng\n",
    "\n",
    "import time\n",
    "\n",
    "import pandas.io.data as web\n",
    "from pykalman import KalmanFilter\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "def get_prices(symbol):\n",
    "    start, end = '2007-05-02', '2016-04-11'\n",
    "    data = web.DataReader(symbol, 'yahoo', start, end)\n",
    "    data=pd.DataFrame(data)\n",
    "    prices=data['Adj Close']\n",
    "    prices=prices.astype(float)\n",
    "    return prices\n",
    "\n",
    "def get_returns(prices):\n",
    "        return ((prices-prices.shift(-1))/prices)[:-1]\n",
    "    \n",
    "def get_data(list):\n",
    "    l = []\n",
    "    for symbol in list:\n",
    "        rets = get_returns(get_prices(symbol))\n",
    "        l.append(rets)\n",
    "    return np.array(l).T\n",
    "        \n",
    "rets = get_data(['C', 'GS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def lstm_iterator(raw_data, batch_size, num_steps, data_size):\n",
    " \n",
    "  raw_data = np.array(raw_data, dtype=np.float32)\n",
    "\n",
    "  data_len = len(raw_data)\n",
    "  batch_len = data_len // batch_size\n",
    "  data = np.zeros([batch_size, batch_len, data_size], dtype=np.float32)\n",
    "  for i in range(batch_size):\n",
    "    data[i] = raw_data[batch_len * i:batch_len * (i + 1),:]\n",
    "\n",
    "  epoch_size = (batch_len - 1) // num_steps\n",
    "\n",
    "  if epoch_size == 0:\n",
    "    raise ValueError(\"epoch_size == 0, decrease batch_size or num_steps\")\n",
    "\n",
    "  for i in range(epoch_size):\n",
    "    x = data[:, i*num_steps:(i+1)*num_steps]\n",
    "    y = data[:, i*num_steps+1:(i+1)*num_steps+1]\n",
    "    yield (x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def run_epoch(session, m, data, eval_op, verbose=False, get_out=False):\n",
    "  \"\"\"Runs the model on the given data.\"\"\"\n",
    "  epoch_size = ((len(data) // m.batch_size) - 1) // m.num_steps\n",
    "  start_time = time.time()\n",
    "  costs = 0.0\n",
    "  iters = 0\n",
    "  state = m.initial_state.eval()\n",
    "  output = []\n",
    "  inp = []\n",
    "  targ = []\n",
    "  for step, (x, y) in enumerate(lstm_iterator(data, m.batch_size, m.num_steps, m.data_size)):\n",
    "    cost, state, _ = session.run([m.cost, m.final_state, eval_op],\n",
    "                                 {m.input_data: x,\n",
    "                                  m.targets: y,\n",
    "                                  m.initial_state: state})\n",
    "    costs += cost\n",
    "    #print(cost)\n",
    "    iters += m.num_steps\n",
    "    if get_out: \n",
    "      output.append(_)\n",
    "      inp.append(x)\n",
    "      targ.append(y)\n",
    "    \n",
    "  if len(output) > 0: \n",
    "    return output, inp, targ\n",
    "\n",
    "  ''' if verbose and step % (epoch_size // 10) == 10:\n",
    "      print(\"%.3f perplexity: %.3f speed: %.0f wps\" %\n",
    "            (step * 1.0 / epoch_size, np.exp(costs / iters),\n",
    "             iters * m.batch_size / (time.time() - start_time)))\n",
    "  '''\n",
    "  return np.exp(costs / iters)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class SmallConfig(object):\n",
    "    \"\"\"Small config.\"\"\"\n",
    "    init_scale = 1.1\n",
    "    learning_rate = .60\n",
    "    max_grad_norm = 5\n",
    "    num_layers = 4\n",
    "    num_steps = 25\n",
    "    hidden_size = 200\n",
    "    max_epoch = 4\n",
    "    max_max_epoch = 13\n",
    "    keep_prob = 1.0\n",
    "    lr_decay = 0.9\n",
    "    batch_size = 20\n",
    "    #vocab_size = 10000\n",
    "    \n",
    "class TestConfig(object):\n",
    "  \"\"\"Tiny config, for testing.\"\"\"\n",
    "  init_scale = 0.1\n",
    "  learning_rate = 1.0\n",
    "  max_grad_norm = 1\n",
    "  num_layers = 4\n",
    "  num_steps = 25\n",
    "  hidden_size = 20\n",
    "  max_epoch = 1\n",
    "  max_max_epoch = 1\n",
    "  keep_prob = 1.0\n",
    "  lr_decay = 0.5\n",
    "  batch_size = 20\n",
    "  #vocab_size = 10000\n",
    "  #data_size= 2\n",
    "\n",
    "def get_config(test=False):\n",
    "    if test:\n",
    "        return TestConfig()\n",
    "    return SmallConfig()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class LSTMModel(object):\n",
    "\n",
    "    def __init__(self, is_training, config):\n",
    "        self.batch_size = batch_size = config.batch_size\n",
    "        self.num_steps = num_steps = config.num_steps\n",
    "        self.data_size = data_size = config.data_size\n",
    "        size = config.hidden_size\n",
    "        \n",
    "        \n",
    "        \n",
    "        position_transform = tf.constant([[-1.,0.,1]])\n",
    "        pb_number = 3\n",
    "        \n",
    "        # set PLACEHOLDERS\n",
    "        self._input_data = tf.placeholder(tf.float32, [batch_size, num_steps, data_size])\n",
    "        self._targets = tf.placeholder(tf.float32, [batch_size, num_steps, data_size])\n",
    "        # set VARIABLES\n",
    "        lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(size, forget_bias=0.0)\n",
    "        cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * config.num_layers)\n",
    "        cell = tf.nn.rnn_cell.InputProjectionWrapper(cell,  size)\n",
    "        cell = tf.nn.rnn_cell.OutputProjectionWrapper(cell, pb_number*data_size)\n",
    "        if is_training and config.keep_prob < 1:\n",
    "            cell = tf.nn.rnn_cell.DropoutWrapper(\n",
    "                cell, output_keep_prob=config.keep_prob)\n",
    "        \n",
    "        # loop through timesteps and record output\n",
    "        outputs = []\n",
    "        self._initial_state = cell.zero_state(batch_size, tf.float32)\n",
    "        state = self._initial_state\n",
    "        with tf.variable_scope(\"RNN\"):\n",
    "            for time_step in range(num_steps):\n",
    "                if time_step > 0: tf.get_variable_scope().reuse_variables()\n",
    "                    \n",
    "                (cell_output, state) = cell(tf.reshape(self._input_data[:, time_step,:], [-1, data_size]), state)\n",
    "                outputs.append(cell_output)\n",
    "        \n",
    "        self._final_state = state    #shape: [20,160]\n",
    "        \n",
    "        #reshape to [batchsize*num_steps, 3*data_size]\n",
    "        self._output = output = tf.reshape(tf.concat(1, outputs), [-1, data_size*pb_number])\n",
    "        targets = tf.reshape(self._targets, [-1,data_size])\n",
    "        \n",
    "        \n",
    "        \n",
    "        pos_one_hot = {}\n",
    "        t_dict = {}\n",
    "        returns = []\n",
    "        for i in range(data_size):\n",
    "            relevant_output = output[:,i*pb_number:(i+1)*pb_number]\n",
    "            sample = tf.argmax(relevant_output) #this should be SAMPLE\n",
    "            pos_one_hot[i] = tf.one_hot(sample, depth= 3, on_value=-1.0, off_value=0.0)\n",
    "            t_dict[i] = tf.matmul(tf.reshape(targets[:,i], [-1,1]) , position_transform)\n",
    "            cross_entropy = tf.nn.softmax_cross_entropy_with_logits(output, pos_one_hot[i])\n",
    "            \n",
    "            returns.append( tf.mul(sample , t_dict[i])  )\n",
    "        returns = tf.reduce_sum(tf.concat(1,returns), 1)\n",
    "        total_return = tf.reduce_prod(returns+1)\n",
    "        \n",
    "        \n",
    "        cross_entropy = tf.nn.softmax_cross_entropy_with_logits(output, tf.nn.softmax(target_menu))\n",
    "        \n",
    "        #pos_one_hot = tf.concat(1, [ v for v in pos_one_hot.values() ])\n",
    "        self._cost = cost  =  total_return #* -100\n",
    "        cost = cost*-1\n",
    "        \n",
    "        #vol = tf.(returns)\n",
    "        #sharpe = total_return/vol\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        '''\n",
    "        self._output = output = tf.reshape(tf.concat(1, outputs), [-1, data_size*3]) # shape: [500, 3]=[??,data_size*3]\n",
    "        self._final_state = state    #shape: [20,160]    =     [hidden size, ??]\n",
    "        targets = tf.reshape(self._targets, [-1,data_size])\n",
    "        \n",
    "        outdict = {}\n",
    "        tm_dict = {}\n",
    "        xe_list = []\n",
    "        for i in range(data_size):\n",
    "            outdict[i] = output[:,i*3:(i+1)*3]\n",
    "            tm_dict[i] = tf.matmul(tf.reshape(targets[:,i], [-1,1]) , position_transform)\n",
    "            xe_list.append(tf.nn.softmax_cross_entropy_with_logits(outdict[i], tf.nn.softmax(tm_dict[i])))\n",
    "        \n",
    "        loss = tf.add_n(xe_list)\n",
    "        self._cost = cost = tf.reduce_sum(loss) / batch_size\n",
    "        \n",
    "        #drets = tf.mul(target_menu, tf.nn.softmax(output) )   # shape: [500, 3]   =   [??, data_size*3]\n",
    "        #max_possible_ret = tf.reduce_max(target_menu, 1)  # shape: [500]\n",
    "\n",
    "        #position = tf.cast(tf.argmax(output, 1) - 1, tf.float32)\n",
    "        #drets_position = tf.mul(position, tf.reshape(self._targets, [-1]))\n",
    "        \n",
    "        #set LOSS\n",
    "        #loss = tf.nn.softmax_cross_entropy_with_logits(outputdict, tf.nn.softmax(target_menu))\n",
    "        #loss=max_possible_ret-tf.reduce_sum(drets,1)#tf.nn.l2_loss(output - tf.reshape(self._targets, [-1, 1]))\n",
    "        \n",
    "        #set COST\n",
    "        #self._cost = cost = tf.reduce_sum(loss) / batch_size\n",
    "        '''\n",
    "        \n",
    "        if not is_training:\n",
    "            return\n",
    "        #TRAINING STEP\n",
    "        self._lr = tf.Variable(0.0, trainable=False)\n",
    "        tvars = tf.trainable_variables()\n",
    "        print(cost)\n",
    "        #grads, _ = tf.clip_by_global_norm(tf.gradients(cost, tvars),  config.max_grad_norm)\n",
    "        #print(grads)\n",
    "        optimizer = tf.train.GradientDescentOptimizer(self.lr)#GradientDescentOptimizer(self.lr)\n",
    "        self._train_op = optimizer.minimize(cost, var_list=tvars)#apply_gradients(zip(grads, tvars))\n",
    "        \n",
    "        \n",
    "        \n",
    "        #####TODO\n",
    "        # whats up with grads?\n",
    "        #normalize input \n",
    "        #try lm nn at the end, see line 31 of https://github.com/sherjilozair/char-rnn-tensorflow/blob/master/model.py\n",
    "        # seq2seq.rnn_decoder and seq2seq.sequence_loss_by_example\n",
    "\n",
    "    def assign_lr(self, session, lr_value):\n",
    "        session.run(tf.assign(self.lr, lr_value))\n",
    "\n",
    "    @property\n",
    "    def input_data(self):\n",
    "        return self._input_data\n",
    "\n",
    "    @property\n",
    "    def targets(self):\n",
    "        return self._targets\n",
    "\n",
    "    @property\n",
    "    def initial_state(self):\n",
    "        return self._initial_state\n",
    "\n",
    "    @property\n",
    "    def cost(self):\n",
    "        return self._cost\n",
    "\n",
    "    @property\n",
    "    def final_state(self):\n",
    "        return self._final_state\n",
    "\n",
    "    @property\n",
    "    def lr(self):\n",
    "        return self._lr\n",
    "\n",
    "    @property\n",
    "    def train_op(self):\n",
    "        return self._train_op\n",
    "    \n",
    "    @property\n",
    "    def output(self):\n",
    "        return self._output\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:<tensorflow.python.ops.rnn_cell.BasicLSTMCell object at 0x10fbcb6a0>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
      "WARNING:tensorflow:<tensorflow.python.ops.rnn_cell.BasicLSTMCell object at 0x10fbcb6a0>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
      "WARNING:tensorflow:<tensorflow.python.ops.rnn_cell.BasicLSTMCell object at 0x11a8cdac8>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
      "WARNING:tensorflow:<tensorflow.python.ops.rnn_cell.BasicLSTMCell object at 0x11a8cdac8>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"model/mul:0\", shape=(), dtype=float32)\n",
      "Epoch: 1 Learning rate: 0.600\n",
      "Epoch: 1 Train Perplexity: 1.026\n",
      "Epoch: 2 Learning rate: 0.600\n",
      "Epoch: 2 Train Perplexity: 1.028\n",
      "Epoch: 3 Learning rate: 0.600\n",
      "Epoch: 3 Train Perplexity: 1.028\n",
      "Epoch: 4 Learning rate: 0.600\n",
      "Epoch: 4 Train Perplexity: 1.028\n",
      "Epoch: 5 Learning rate: 0.600\n",
      "Epoch: 5 Train Perplexity: 1.029\n",
      "Epoch: 6 Learning rate: 0.540\n",
      "Epoch: 6 Train Perplexity: 1.029\n",
      "Epoch: 7 Learning rate: 0.486\n",
      "Epoch: 7 Train Perplexity: 1.029\n",
      "Epoch: 8 Learning rate: 0.437\n",
      "Epoch: 8 Train Perplexity: 1.029\n",
      "Epoch: 9 Learning rate: 0.394\n",
      "Epoch: 9 Train Perplexity: 1.029\n",
      "Epoch: 10 Learning rate: 0.354\n",
      "Epoch: 10 Train Perplexity: 1.029\n",
      "Epoch: 11 Learning rate: 0.319\n",
      "Epoch: 11 Train Perplexity: 1.029\n",
      "Epoch: 12 Learning rate: 0.287\n",
      "Epoch: 12 Train Perplexity: 1.029\n",
      "Epoch: 13 Learning rate: 0.258\n",
      "Epoch: 13 Train Perplexity: 1.029\n",
      "Epoch: 14 Learning rate: 0.232\n",
      "Epoch: 14 Train Perplexity: 1.029\n",
      "Epoch: 15 Learning rate: 0.209\n",
      "Epoch: 15 Train Perplexity: 1.029\n",
      "Epoch: 16 Learning rate: 0.188\n",
      "Epoch: 16 Train Perplexity: 1.029\n",
      "Epoch: 17 Learning rate: 0.169\n",
      "Epoch: 17 Train Perplexity: 1.029\n",
      "Epoch: 18 Learning rate: 0.153\n",
      "Epoch: 18 Train Perplexity: 1.029\n",
      "Epoch: 19 Learning rate: 0.137\n",
      "Epoch: 19 Train Perplexity: 1.029\n",
      "Epoch: 20 Learning rate: 0.124\n",
      "Epoch: 20 Train Perplexity: 1.029\n",
      "Epoch: 21 Learning rate: 0.111\n",
      "Epoch: 21 Train Perplexity: 1.029\n",
      "Epoch: 22 Learning rate: 0.100\n",
      "Epoch: 22 Train Perplexity: 1.029\n",
      "Epoch: 23 Learning rate: 0.090\n",
      "Epoch: 23 Train Perplexity: 1.029\n",
      "Epoch: 24 Learning rate: 0.081\n",
      "Epoch: 24 Train Perplexity: 1.029\n",
      "Epoch: 25 Learning rate: 0.073\n",
      "Epoch: 25 Train Perplexity: 1.029\n",
      "Epoch: 26 Learning rate: 0.066\n",
      "Epoch: 26 Train Perplexity: 1.029\n",
      "Epoch: 27 Learning rate: 0.059\n",
      "Epoch: 27 Train Perplexity: 1.029\n",
      "Epoch: 28 Learning rate: 0.053\n",
      "Epoch: 28 Train Perplexity: 1.029\n",
      "Epoch: 29 Learning rate: 0.048\n",
      "Epoch: 29 Train Perplexity: 1.029\n",
      "Epoch: 30 Learning rate: 0.043\n",
      "Epoch: 30 Train Perplexity: 1.029\n",
      "Epoch: 31 Learning rate: 0.039\n",
      "Epoch: 31 Train Perplexity: 1.029\n",
      "Epoch: 32 Learning rate: 0.035\n",
      "Epoch: 32 Train Perplexity: 1.029\n",
      "Epoch: 33 Learning rate: 0.031\n",
      "Epoch: 33 Train Perplexity: 1.029\n",
      "Epoch: 34 Learning rate: 0.028\n",
      "Epoch: 34 Train Perplexity: 1.029\n",
      "Epoch: 35 Learning rate: 0.025\n",
      "Epoch: 35 Train Perplexity: 1.029\n",
      "Epoch: 36 Learning rate: 0.023\n",
      "Epoch: 36 Train Perplexity: 1.029\n",
      "Epoch: 37 Learning rate: 0.021\n",
      "Epoch: 37 Train Perplexity: 1.029\n",
      "Epoch: 38 Learning rate: 0.019\n",
      "Epoch: 38 Train Perplexity: 1.029\n",
      "Epoch: 39 Learning rate: 0.017\n",
      "Epoch: 39 Train Perplexity: 1.029\n",
      "Epoch: 40 Learning rate: 0.015\n",
      "Epoch: 40 Train Perplexity: 1.029\n"
     ]
    }
   ],
   "source": [
    "\n",
    "raw_data = rets, rets[:2200], rets[2200:]\n",
    "train_data, valid_data, test_data = raw_data\n",
    "#train_data =valid_data = test_data =  raw_data\n",
    "\n",
    "config = eval_config = get_config()\n",
    "eval_config = get_config(test=True)\n",
    "eval_config.batch_size = 1\n",
    "eval_config.num_steps = 1\n",
    "\n",
    "config.data_size = eval_config.data_size = rets.shape[1]\n",
    "\n",
    "with tf.Graph().as_default(), tf.Session() as session:\n",
    "    initializer = tf.random_uniform_initializer(-config.init_scale,\n",
    "                                            config.init_scale)\n",
    "    with tf.variable_scope(\"model\", reuse=None, initializer=initializer):\n",
    "        m = LSTMModel(is_training=True, config=config)\n",
    "    with tf.variable_scope(\"model\", reuse=True, initializer=initializer):\n",
    "        mvalid = LSTMModel(is_training=False, config=config)\n",
    "        #mtest = LSTMModel(is_training=False, config=eval_config)\n",
    "\n",
    "    tf.initialize_all_variables().run()\n",
    "\n",
    "    for i in range(40):#config.max_max_epoch):\n",
    "        lr_decay = config.lr_decay ** max(i - config.max_epoch, 0.0)\n",
    "        m.assign_lr(session, config.learning_rate * lr_decay)\n",
    "\n",
    "        print(\"Epoch: %d Learning rate: %.3f\" % (i + 1, session.run(m.lr)))\n",
    "        train_perplexity = run_epoch(session, m, train_data, m.train_op,\n",
    "                                   verbose=True)\n",
    "\n",
    "        print(\"Epoch: %d Train Perplexity: %.3f\" % (i + 1, train_perplexity))\n",
    "        #valid_perplexity = run_epoch(session, mvalid, valid_data, tf.no_op())\n",
    "        #print(\"Epoch: %d Valid Perplexity: %.3f\" % (i + 1, valid_perplexity))\n",
    "\n",
    "    test_output, test_inp, test_targ = run_epoch(session, mvalid, train_data, mvalid.output, get_out = True)\n",
    "    #print(test_perplexity)\n",
    "    #print(\"Test Perplexity: %.3f\" % test_perplexity)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11cd64668>,\n",
       " <matplotlib.lines.Line2D at 0x11cd64a58>,\n",
       " <matplotlib.lines.Line2D at 0x11cd64d68>,\n",
       " <matplotlib.lines.Line2D at 0x11cd6b048>,\n",
       " <matplotlib.lines.Line2D at 0x11cd6b2e8>,\n",
       " <matplotlib.lines.Line2D at 0x11cd6b588>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lZmdn+dSnPsX58+d53/vex1e+8pVej00IIYS46XTVmIeGhti3bx+6rrNnzx4s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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fa089b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(test_output[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1, -1,  0, ...,  0, -1, -1])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_positions(test_output, 2)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1217bb860>]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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btmzdujVjxozJokWL8ulPf3pkB38HPPjgg7nxxhuzcePGPPvss//zvvT396ejoyN79uxJ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\nJeIAUCgRB4BCiTgAFErEAaBQIg4Ahfp/l/566ePKI2IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1209e0208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(get_positions(test_output, 2)[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (3000,2) (2000,2) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-80-85b95d90264f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;31m#return [np.cumprod((pos*tar)[:,i] + 1) for i in range(pos.shape[1])]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m \u001b[0mget_equitycurve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_positions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_targ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;31m#plot(get_equitycurve(get_positions(test_output, 2), get_target(test_targ)))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-80-85b95d90264f>\u001b[0m in \u001b[0;36mget_equitycurve\u001b[0;34m(pos, tar)\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0mks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m         \u001b[0mrets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mtar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m         \u001b[0meq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumprod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mtar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0mls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (3000,2) (2000,2) "
     ]
    }
   ],
   "source": [
    "pb_number = 2\n",
    "\n",
    "def get_positions(output, ds):\n",
    "    ordered = np.concatenate([x.reshape(-1, 25, pb_number*ds) for x in test_output], 1).reshape(-1,pb_number*ds)\n",
    "    ls = []\n",
    "    for i in range(ds):\n",
    "        ls.append(np.argmax(ordered[:,i*pb_number:(i+1)*pb_number], 1)-1)\n",
    "    return np.concatenate([x.reshape([-1,1]) for x in ls],1)\n",
    "    #return np.argmax(np.concatenate(output), 1)-1\n",
    "    #return np.argmax(np.concatenate([x.reshape(-1, 25, 3*2) for x in output], 1).reshape(-1,3*2), 1)-1\n",
    "\n",
    "def get_target(targ):\n",
    "    #return np.concatenate([(x.reshape(-1)) for x in test_targ])\n",
    "    return np.concatenate(targ, 1).reshape(-1,2)\n",
    "\n",
    "def get_equitycurve(pos, tar):\n",
    "    ls = []\n",
    "    ks = []\n",
    "    for i in range(pos.shape[1]):\n",
    "        rets = (pos*tar)[:,i]\n",
    "        eq = np.cumprod((pos*tar)[:,i] + 1)\n",
    "        ls.append(eq)\n",
    "        ks.append(rets)\n",
    "        plot(eq)\n",
    "    show()\n",
    "    plot(np.cumprod(np.sum(ks,0)+1))\n",
    "    return \n",
    "        \n",
    "    #return [np.cumprod((pos*tar)[:,i] + 1) for i in range(pos.shape[1])]\n",
    "\n",
    "get_equitycurve(get_positions(test_output, 2), get_target(test_targ))\n",
    "\n",
    "#plot(get_equitycurve(get_positions(test_output, 2), get_target(test_targ)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'eq' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-73-c4ecaab94ecb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meq\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'eq' is not defined"
     ]
    }
   ],
   "source": [
    "plot(eq[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pos' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-80-4452cc5f64ed>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m125\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'pos' is not defined"
     ]
    }
   ],
   "source": [
    "plot(pos[100:125])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x135a37ef0>,\n",
       " <matplotlib.lines.Line2D at 0x135a18320>,\n",
       " <matplotlib.lines.Line2D at 0x135a185f8>]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sfTGOaoxLajG2jCU5zGkZpxPjmQkDQUiw0n5qleakgRCChU/+A+Y/9lEIkoT973gn6m96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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13755ae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=0\n",
    "n = 6\n",
    "plot(test_output[0][25:50].reshape(-1,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x127872d30>]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7ZMR9jXTG4HF9gLaWQE1STJuFBfltO3BsrKBEqxfTNBkeTxTUywYr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\nQhPz/wO9iAfDrqR70gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13190d940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(np.cumprod((np.argmax(test_output[0],1)-1) * test_targ[0].reshape(-1)+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 3)"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_output[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -2.59504002e-03,   3.88248474e-03,   2.96951691e-03,\n",
       "        -7.44601479e-03,   1.69992819e-02,   1.69177400e-03,\n",
       "         4.70721256e-03,   1.32415968e-03,  -4.01590280e-02,\n",
       "         2.00323318e-03,  -3.64963547e-03,   2.90904660e-03,\n",
       "        -4.37630061e-03,   1.27090258e-03,   1.45425892e-03,\n",
       "        -3.45899235e-03,   3.80993495e-03,  -5.28141344e-03,\n",
       "         1.28622735e-02,  -3.66948894e-04,   6.60424680e-03,\n",
       "         4.61680628e-03,   1.05751613e-02,   1.51884034e-02,\n",
       "        -1.54226488e-02,   1.00798355e-02,  -2.50318591e-02,\n",
       "         9.31291468e-03,   3.40505913e-02,   3.13581303e-02,\n",
       "         2.90240673e-03,   1.85849052e-02,   3.35386321e-02,\n",
       "        -5.90179348e-03,   3.98970395e-03,   1.46088153e-02,\n",
       "         1.41081400e-02,  -3.39558572e-02,  -1.40747463e-03,\n",
       "         1.35862939e-02,   4.98695113e-03,   6.89071193e-02,\n",
       "         2.02545281e-02,   4.85024862e-02,   2.28412598e-02,\n",
       "         4.76054214e-02,   1.52649088e-02,  -6.07902650e-03,\n",
       "        -1.41994311e-02,  -6.94071725e-02,   1.44857559e-02,\n",
       "        -1.02400750e-01,  -3.42222117e-02,  -6.44606585e-03,\n",
       "         5.84969968e-02,   1.17914136e-02,   4.40568626e-02,\n",
       "        -2.83245016e-02,  -1.12978511e-01,  -7.55034992e-03,\n",
       "        -1.41549092e-02,   1.14942668e-02,  -2.15946753e-02,\n",
       "         3.41463201e-02,   7.57577177e-03,  -5.51316328e-03,\n",
       "         1.47617059e-02,   3.63869853e-02,  -1.28831351e-02,\n",
       "        -2.80701462e-02,  -2.51706913e-02,  -4.49438319e-02,\n",
       "         3.18599096e-03,  -3.59566044e-03,   1.95063446e-02,\n",
       "         2.92171314e-02,  -1.30606899e-02,  -1.56950857e-02,\n",
       "        -5.29801659e-02,   4.71703103e-03,  -6.31918060e-03,\n",
       "        -2.61643101e-02,   6.68026805e-02,  -4.20764834e-02,\n",
       "        -6.55479804e-02,   7.08661154e-02,   1.05932206e-02,\n",
       "         3.74730583e-03,   3.49274538e-02,   1.51447728e-01,\n",
       "        -3.34646031e-02,   1.09206326e-01,  -1.86742663e-01,\n",
       "        -2.40240246e-01,   3.09927091e-02,   9.99559183e-04,\n",
       "         5.15257046e-02,  -2.37341747e-02,  -3.81246470e-02,\n",
       "         1.19106695e-01,  -2.81690117e-02,   5.20548299e-02,\n",
       "        -8.67059268e-03,  -1.14612589e-02,  -1.07648715e-01,\n",
       "        -1.02302041e-02,   1.51898727e-01,  -1.01492584e-01,\n",
       "         2.16802955e-02,   3.32409404e-02,   1.23209193e-01,\n",
       "         4.90195863e-02,   1.37457058e-01,   2.23107591e-01,\n",
       "        -9.74359140e-02,  -2.14953303e-01,   3.07692271e-02,\n",
       "         2.38095205e-02,   3.90243948e-01,   1.99999988e-01,\n",
       "        -1.66666657e-02,   7.37704411e-02,   9.73451659e-02,\n",
       "        -9.80397034e-03,  -1.94174722e-02,   3.71747166e-02,\n",
       "        -7.33590499e-02,  -5.03597818e-02,  -8.56164098e-02,\n",
       "         4.41640653e-02,   3.30027984e-03,   7.61589780e-02,\n",
       "         5.01792058e-02,  -5.66037856e-02,   1.07142664e-02,\n",
       "         1.44405039e-02,   1.46519765e-02,  -1.04089245e-01,\n",
       "        -8.41750875e-02,   2.48447489e-02,  -9.55415517e-03,\n",
       "        -3.15452600e-03,  -2.20126249e-02,  -1.01538435e-01,\n",
       "        -6.14525378e-02,  -1.31578799e-02,  -2.33766604e-02,\n",
       "         6.34517521e-02,  -7.85907060e-02,  -2.01005004e-02,\n",
       "         3.78378890e-02,   3.08988318e-02,   7.24637732e-02,\n",
       "        -6.24999925e-02,  -5.88238193e-03,   2.33917814e-02,\n",
       "         1.49700725e-02,  -1.82370488e-02,  -1.19403275e-02,\n",
       "         5.89970406e-03,   1.48368087e-02,   3.01212375e-03,\n",
       "        -2.71903761e-02,  -3.82352769e-02,  -3.11615430e-02,\n",
       "        -2.74721067e-03,   1.64382998e-02,  -1.11419754e-02,\n",
       "         3.03029865e-02,   5.68184629e-03,  -2.85709929e-03,\n",
       "         2.56409533e-02,  -3.50877456e-02,   2.25989521e-02,\n",
       "         5.49132191e-02,   0.00000000e+00,  -2.11639926e-02,\n",
       "        -4.14508544e-02,   1.49254464e-02,   2.77777389e-02,\n",
       "        -1.81818567e-02,  -1.02040032e-02,   4.29292023e-02,\n",
       "         3.95778455e-02,  -2.19779909e-02,  -4.03225906e-02,\n",
       "        -7.75192399e-03,   5.12823043e-03,   0.00000000e+00,\n",
       "        -2.83505786e-02,   0.00000000e+00,   7.51888426e-03,\n",
       "        -1.26262745e-02,  -2.49382830e-03,   1.99004952e-02,\n",
       "         1.26903411e-02,   2.82776952e-02,  -4.23280932e-02,\n",
       "        -1.52283991e-02,   6.75000325e-02,   0.00000000e+00,\n",
       "         4.79616271e-03,  -4.81927674e-03,  -4.79618693e-03,\n",
       "        -3.34128365e-02,  -3.69515456e-02,   1.11358445e-02,\n",
       "         3.15315723e-02,  -2.79069971e-02,   1.35747045e-02,\n",
       "         1.60549860e-02,  -6.99299434e-03,   2.31481455e-02,\n",
       "         7.10899197e-03,  -2.62529440e-02,   6.97670830e-03,\n",
       "         2.10773405e-02,   1.91387311e-02,  -1.70731805e-02,\n",
       "         1.43885361e-02,  -9.73240845e-03,  -1.20481793e-02,\n",
       "        -2.38095224e-02,  -2.79069971e-02,  -6.78731827e-03,\n",
       "         2.17870390e-03,   4.36683372e-03,   6.57893578e-03,\n",
       "        -4.41503292e-03,   1.09890206e-02,   1.55555652e-02,\n",
       "         2.25730194e-03,   0.00000000e+00,  -2.48868410e-02,\n",
       "        -8.83006584e-03,   4.37638909e-03,   6.59339502e-03,\n",
       "         2.21244502e-03,   0.00000000e+00,  -1.77384485e-02,\n",
       "         0.00000000e+00,   2.17865109e-02,  -6.68150233e-03,\n",
       "         0.00000000e+00,   8.84957891e-03,  -8.92859325e-03,\n",
       "         2.30088774e-02,  -9.05829016e-04,   2.89593358e-02,\n",
       "         1.16495918e-02,  -1.25692999e-02,  -4.38116603e-02,\n",
       "        -1.39908856e-02,   4.41531837e-02,   2.81486567e-02,\n",
       "         5.23579568e-02,   6.11285865e-02,  -4.25709821e-02,\n",
       "        -6.96557313e-02,  -1.01048052e-02,   3.96442860e-02,\n",
       "        -3.78086194e-02,   4.75835986e-02,   9.79703739e-02,\n",
       "        -5.53872250e-02,  -1.31201297e-02,  -5.30150458e-02,\n",
       "         5.34204915e-02,  -7.55176991e-02,  -5.09626269e-02,\n",
       "        -4.88505848e-02,   5.34247085e-02,  -2.74964049e-02,\n",
       "         1.65492818e-02,  -6.98173866e-02,   2.99760066e-02,\n",
       "        -1.08157787e-02,   1.10058226e-02,  -1.79289375e-02,\n",
       "        -1.67020801e-02,   4.77895886e-03,  -2.43097395e-02,\n",
       "         8.79066996e-04,   1.23166433e-02,   4.63183485e-02,\n",
       "        -3.48693281e-02,  -2.28639673e-02,  -5.88238053e-03,\n",
       "        -2.63159699e-03,  -6.29921183e-02,   3.40192579e-02,\n",
       "        -3.01051326e-02,  -1.15797492e-02,  -1.30825741e-02,\n",
       "        -2.44822185e-02,   7.35301198e-03,   2.38094479e-02,\n",
       "        -6.23304723e-03,  -8.07970297e-03,   1.73658021e-02,\n",
       "         5.86080458e-03,   3.68452806e-04,   1.29008247e-02,\n",
       "         2.20395997e-02,  -4.66004014e-02,  -4.23357673e-02,\n",
       "        -1.15546361e-02,   1.03839755e-03,   0.00000000e+00,\n",
       "        -1.38598087e-03,   4.15222254e-03,   3.47467110e-04,\n",
       "         1.04276370e-03,  -2.78357067e-03,  -7.28664733e-03,\n",
       "        -3.27247232e-02,  -2.50166692e-02,   7.80994073e-03,\n",
       "         2.95178834e-02,  -8.11086409e-03,   4.69324272e-03,\n",
       "         1.17885014e-02,  -1.94273982e-02,   8.69276747e-03,\n",
       "        -2.02359585e-03,   2.35518324e-04,   9.89161059e-03,\n",
       "        -1.87916346e-02,   1.14406003e-02,   2.83418410e-03,\n",
       "        -8.28987733e-03,   2.11416534e-03,   2.91901696e-02,\n",
       "        -1.01842564e-02,  -2.88042123e-03,  -5.74441720e-03,\n",
       "        -1.85625628e-02,  -2.57011503e-03,   1.32836048e-02,\n",
       "         4.25132364e-03,   4.98098601e-03,  -5.24555845e-03,\n",
       "        -2.03985013e-02,   1.88284852e-02,  -1.68206487e-02,\n",
       "        -3.02891503e-03,   4.18119458e-03,   4.43196855e-03,\n",
       "        -1.10122003e-02,  -2.78099049e-02,  -2.84436764e-03,\n",
       "        -1.31685026e-02,   9.59807727e-03,   3.29092853e-02,\n",
       "         2.15031952e-02,   3.05099115e-02,  -3.43310162e-02,\n",
       "        -1.29787168e-02,  -1.40726352e-02,   6.42082235e-03,\n",
       "        -5.83695108e-03,   2.07249983e-03,   9.96890571e-03,\n",
       "        -1.80407073e-02,  -2.03997809e-02,  -1.39337434e-02,\n",
       "         1.15514630e-02,  -8.26114137e-03,  -1.53876953e-02,\n",
       "        -1.96811706e-02,  -3.86022468e-04,   7.71766412e-04,\n",
       "        -1.73778366e-02,   6.45283842e-03,  -1.24164540e-02,\n",
       "         7.81703275e-03,  -1.89087708e-02,  -1.31451963e-02,\n",
       "        -1.67907123e-02,   5.25433291e-03,  -7.54594686e-04,\n",
       "         2.45054089e-03,   5.66890463e-03,   9.31202341e-03,\n",
       "         1.72645552e-03,   1.88316554e-02,  -8.42147693e-03,\n",
       "        -1.20411506e-02,   7.10033579e-03,   1.99072957e-02,\n",
       "        -3.94402072e-03,  -1.17857184e-03,   1.37333910e-03,\n",
       "         4.12580185e-03,  -2.50542685e-02,   1.53960939e-03,\n",
       "        -6.36078324e-03,  -3.83070856e-03,  -3.81607097e-04,\n",
       "         1.52586529e-03,   2.92280177e-03,   6.28111535e-04,\n",
       "         1.15231285e-02,   1.73802339e-02,  -7.33389985e-03,\n",
       "        -9.42180492e-03,   3.18194856e-03,  -5.95868006e-03,\n",
       "        -3.17321881e-03,   6.32642629e-03,   1.27334092e-02,\n",
       "         1.71973300e-03,  -7.10598100e-03,   4.70389472e-03,\n",
       "        -6.44466421e-03,  -6.18997356e-03,  -3.18205520e-03,\n",
       "        -5.92087908e-03,   5.25540859e-03,   8.45318835e-04,\n",
       "        -6.13367744e-03,  -3.99408350e-03,  -9.00335982e-03,\n",
       "         6.43280754e-03,  -1.56641454e-02,   1.93190333e-02,\n",
       "        -2.47708391e-02,   2.66463906e-02,   2.01407671e-02,\n",
       "         4.19077044e-03,  -3.14629339e-02,   3.47775742e-02,\n",
       "        -1.40902121e-03,  -6.43214490e-03,  -9.18712933e-03,\n",
       "        -1.46447904e-02,   1.11175980e-02,  -1.40039362e-02,\n",
       "        -7.58606056e-03,   4.05404437e-03,  -1.64760407e-02,\n",
       "        -4.00462467e-03,  -9.68844164e-03,  -7.14953290e-03,\n",
       "         1.12082832e-03,   5.23661450e-03,  -1.56044625e-02,\n",
       "         5.55330829e-04,   4.44530137e-03,  -2.60464917e-03,\n",
       "        -2.79383897e-03,   2.22879928e-03,   2.49441527e-02,\n",
       "        -1.14547303e-02,   9.43752378e-03,   1.02896634e-02,\n",
       "         1.07816188e-02,   3.69799021e-03,   3.71163758e-03,\n",
       "        -1.25490064e-02,   2.32375204e-03,  -1.94097043e-03,\n",
       "        -4.64938255e-03,   4.62786574e-03,   1.93721033e-03,\n",
       "        -7.18164910e-03,  -4.62520961e-03,  -5.75483218e-03,\n",
       "        -4.38678684e-03,  -1.32928521e-03,  -9.10296291e-03,\n",
       "        -1.52227273e-02,   1.75860897e-02,  -2.44960329e-03,\n",
       "         1.35338418e-02,   3.12669948e-02,   6.08208738e-02,\n",
       "         9.13783628e-03,  -4.81154248e-02,  -2.21882220e-02,\n",
       "         2.99400999e-03,  -3.75377014e-03,   4.74944152e-02,\n",
       "        -1.59010887e-02,  -7.72965723e-04,   2.31704991e-02,\n",
       "        -2.09527835e-02,   1.49080437e-02,  -3.73423472e-03,\n",
       "        -5.87409013e-04,   2.34831544e-03,  -2.00078525e-02,\n",
       "        -1.13461660e-02,   1.78740900e-02,   2.63310950e-02,\n",
       "        -8.35153554e-03,   6.50756806e-03,   5.16081462e-03,\n",
       "         1.97525416e-02,  -2.89028697e-02], dtype=float32)"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_targ[0].reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.00259504],\n",
       "       [ 0.00388248],\n",
       "       [ 0.00296952],\n",
       "       [-0.00744601],\n",
       "       [ 0.01699928],\n",
       "       [ 0.00169177],\n",
       "       [ 0.00470721],\n",
       "       [ 0.00132416],\n",
       "       [-0.04015903],\n",
       "       [ 0.00200323],\n",
       "       [-0.00364964],\n",
       "       [ 0.00290905],\n",
       "       [-0.0043763 ],\n",
       "       [ 0.0012709 ],\n",
       "       [ 0.00145426],\n",
       "       [-0.00345899],\n",
       "       [ 0.00380993],\n",
       "       [-0.00528141],\n",
       "       [ 0.01286227],\n",
       "       [-0.00036695],\n",
       "       [ 0.00660425],\n",
       "       [ 0.00461681],\n",
       "       [ 0.01057516],\n",
       "       [ 0.0151884 ],\n",
       "       [-0.01542265]], dtype=float32)"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_targ[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.00259504,  0.00388248,  0.00296952, -0.00744601,  0.01699928,\n",
       "        0.00169177,  0.00470721,  0.00132416, -0.04015903,  0.00200323,\n",
       "       -0.00364964,  0.00290905, -0.0043763 ,  0.0012709 ,  0.00145426,\n",
       "       -0.00345899,  0.00380993, -0.00528141,  0.01286227, -0.00036695,\n",
       "        0.00660425,  0.00461681,  0.01057516,  0.0151884 , -0.01542265], dtype=float32)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_targ[0][0].reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12e4df7b8>]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/ePfCIev/LxuraG51c+vySSGNyRTm2VizfBLPv1/FK1uqufuG4U83PnziPK9t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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e0c1dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(test_inp[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x128dbff98>]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Otg2fPDaS8fo3kkxFwWW7NSfamFqWvC+2sQSUeL+2/AXoU0rtJDDB+3Wl1N8o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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e0c1128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(test_targ[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.50313473, -9.14566231,  0.35615453],\n",
       "       [-0.37630498, -7.11112261, -0.70588076],\n",
       "       [-0.37363619, -7.10911226, -0.7056942 ],\n",
       "       [-0.37376261, -7.10809135, -0.70627677],\n",
       "       [-0.37625742, -7.13504505, -0.69292498],\n",
       "       [-0.37384361, -7.10783291, -0.70645559],\n",
       "       [-0.37367171, -7.107337  , -0.7067076 ],\n",
       "       [-0.3738786 , -7.10826683, -0.70620131],\n",
       "       [-0.3762539 , -7.13502169, -0.69293654],\n",
       "       [-0.37384126, -7.10775185, -0.70650387],\n",
       "       [-0.37365335, -7.1073513 , -0.70669627],\n",
       "       [-0.37384212, -7.10807276, -0.70630753],\n",
       "       [-0.37625468, -7.13503265, -0.69293034],\n",
       "       [-0.37382513, -7.10767174, -0.70654678],\n",
       "       [-0.37368315, -7.1073904 , -0.70667815],\n",
       "       [-0.37379837, -7.10780811, -0.70645642],\n",
       "       [-0.37611288, -7.13384342, -0.69351137],\n",
       "       [-0.3738367 , -7.10773849, -0.70650995],\n",
       "       [-0.3736918 , -7.10737848, -0.70668697],\n",
       "       [-0.37380508, -7.10784388, -0.70643675],\n",
       "       [-0.37624282, -7.13494444, -0.69297278],\n",
       "       [-0.37383717, -7.10773134, -0.70651484],\n",
       "       [-0.37368673, -7.10735941, -0.70669711],\n",
       "       [-0.3738032 , -7.10783863, -0.70643926],\n",
       "       [-0.37602016, -7.13306904, -0.69388962]], dtype=float32)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_output[0][:25,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11a744c88>]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8jnc0Rr+eRQws7cKnKdhesXtxHpXV9d4r68Boj1hBatq4vixZG5rXeTlZIQVs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1279b14e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(rets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "inv = test_output[0][25*x*1:25*1*(x+1)].reshape(-1,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 6)"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_output[0][0:25*6].reshape(-1,6).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'module' object has no attribute 'mul'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-256-e865e56656d6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'module' object has no attribute 'mul'"
     ]
    }
   ],
   "source": [
    "np.mul(inv[:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -6.98216486,  -6.98215246,  -6.98232746,  -6.98221302,\n",
       "        -6.98204231,  -6.98219585,  -6.98207426,  -6.98217106,\n",
       "        -6.98215723,  -6.98219633,  -6.98216343,  -6.98217678,\n",
       "        -6.98221302,  -6.98215961,  -6.98213148,  -6.9821701 ,\n",
       "        -6.98215151,  -6.98215818,  -6.98216295,  -6.98211956,\n",
       "       -14.57940769, -14.57942486, -14.57956505, -14.57947636, -14.57931995], dtype=float32)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(inv,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
